Today, we start a series of posts about Machine Learning Workflows supported in OpendTect. Each workflow is presented in the form of a short video. We will show...
Today, we start a series of posts about Machine Learning Workflows supported in OpendTect. Each workflow is presented in the form of a short video. We will show that you do not have to be a data science expert to use Machine Learning solutions in day-to-day seismic interpretation work. Some videos show workflows from our original Neural Networks plugin, which is now an integral part of the Machine Learning plugin . Other videos describe new workflows for deep learning algorithms such as CNNs and Unets. Amongst others we will show that labels can be created with a paintbrush (image-to-point applications) and by drawing polygons (image-to-image applications).
We kick-off the series with an old-time favorite: Quick UVQ Waveform Segmentation. This very simple but extremely useful workflow visualizes seismic patterns along a mapped horizon. The user selects a time-window along a mapped horizon to be segmented into a user-defined number of segments. The network automatically finds the cluster centers in a training phase. It then follows up by applying the trained network to all trace segments along the horizon resulting in two output grids: a segmentation grid and a match grid expressing the confidence in the segmentation result on a scale of 0 to 1.
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